License

The zip-file contains the following files for visualizing distributions:
- distributionPlot.m: main function that allows creating violin plots
- myHistogram.m: generate histograms with 'ideal' bin width given the number of data points and the spread (Freedman-Diaconis rule). Note that for integer-valued data, each integer gets its own bin.

DistributionPlot allows visualizing multiple distributions side by side. It is useful for skewed unimodal data and indispensable for multimodal data. DistributionPlot is especially useful for showing the time evolution of a distribution.

Comments and Ratings (70)

Works well. The examples are very helpful. Plotting different distributions on the left and right of a single violin was unclear at first, but the example in the comments made it clear that the widthDiv parameter is necessary here. Perhaps a bit more description on this feature would be helpful. My only minor issue with this plotting tool is the limited aesthetics ability of the plots for adjusting edge and face color and transparency.

Nice submission. I am new to violin plots. I have a plot I would like to generate. I have results from pre and post contrast agent I would like to use a single violin plot to show them i.e left side of the single plot pre and right side of the plot post agent. Does your script allow for such plots?

Thanks for sharing!
Did you thought about renaming your function 'histogram(varargin)'? It might shadow MATLAB's function 'histogram' Introduced in R2014b, which requires different input. This might cause confusion.

There appears to be a sizable bug when using strings (matrices or cell arrays) as categories. The data ignore the order of the categories, leading to arbitrary data distributions. Here's an example. Results are bad when the categories are 'a' and 'b', results are fine if the categories are 1 and 2.
a=randn(1000,1)+(1:1000).';
b=[repmat('a',500,1);repmat('b',500,1)];
% b=[repmat(1,500,1);repmat(2,500,1)];
figure
distributionPlot(a,'histOpt',0,'addspread',1,'groups',b,'showMM',6)
figure
distributionPlot(a,'histOpt',0,'addspread',1,'groups',flipud(b),'showMM',6)

Thanks for the code!
I found an error when wanting to use legends with distributionPlot.m. The first output handles (patch) return an integer instead of a Patch (CS). To fix it is quite simple:
line 44: hh = {}; % Instead of NaN(nData,1);
line 729: hh{iData} =...
line 731: hh{iData} =...

I don't know if there is place to suggest changes in Matlab File Exchange. I hope the author or somebody else can do this fix.

One little suggestion: it would be amazing being able to constrain the density estimation within a given interval, so not to obtain "undesired tails" that trespass the desired lower and upper bound values - for example if you are plotting the violin plot from a set of scores that can only range from, say, 1 to 100, in order to prevent the tails of the violin spanning from values smaller than 1 and larger than 100.

Great peace of code, just I was looking for. However, I have a question: Is there any way to normalize histograms across comparison (i.e. when using the option "widthDiv") such that both the left and right distributions will have the same area?

Sorry, this was my mistake in a way. If the data vector is a row, not a column, the result of the grouping are identical datasets. Could be good to put a check in here, to verify that the dimensions of the data and the grouping variable are the same.

Can anyone confirm that this works with grouping the variable (and under which MatLab version)? I am having problems. Maybe an example would be good to confirm this. I get identical group data after grouping (R2017a).

Displaying distributional differences provide more information of the samples and are very useful when distance from zero is meaningless.
Furthermore, the option to overlay the mean, SEM, sd and percentiles helps us better interpret the statistical analyses.
Overall, an invaluable option to the classic barplots and boxplots.

This is a great function. However I want to discriminate between two quite different distributions. I have a problem getting the Total area under the respective curves to be equal (to a nominal 1) for separate datasets (even with the same number of observations). Eg, Say I want to plot U and V left and right respectively where
U = normrnd(3.3,1.0,100,1);
V = normrnd(2.0,0.3,100,1);

then no matter what I do, they don't look anywhere near equal. Any ideas? or have I missed something obvious?

This is a great tool... It would be nice if some of the functionality could be achieved without requiring toolboxes (e.g. I've cobbled together the code to do the smoothed histograms without the spline toolbox, using files from FEX).

@Jonas, I didn't find if there is a way to change the width of dots spread (addSpread is 1). It doesn't seem to depend on distWidth. If I don't show the density (color is white), the distance between groups is quite large. Thanks.

Overall, this is a great function, and I use it quite often to analyze model ensemble output. A few enhancements that could be nice:

- Add the option to display in a horizontal orientation.

- Add the option to filter outliers when calculating bin widths and kernal densities. Could also be nice to display these as points, as in boxplot, rather than connecting them via long lines to the main histogram.

- This is an edge case, but the function will error under the addSpread option if a column/group contains only NaNs and/or Infs.

This is very good. I've just included some plots in a report. Thank you. Possibly you could add an extra feature within the options of 'showMM' = 6, say, which would be to draw a horizontal line of linewidth 2 for the median, and 25 & 75 pctiles at linewidth 1.

@Jonas: Thanks for the answer. May I suggest a new feature? It would be nice to draw histogram at certain direction. Currently it's only centered, but also can be left- or right- directed. All you need to change is xBase variable at line 401: 0.5 to 0 for left direction, -0.5 to 0 for right direction. For someone it's easier to understand when the distributions looks like turned histograms.

@Jonas: I have problem with smoothing (histOpt=1) when all values for a group are the same. In this case the distribution plot is very wide comparing to the same data with a little variance.
For example:
x = zeros(10,1);
y = x+randn(10,1)*0.1;
distributionPlot({x,y},'histOpt',1,'addSpread',1)

The same happens with a few outliers in x. I understand it's probably how ksdensity function works. But can you do anything to make the above cases comparable?

This works quite well, giving a very interesting data presentation method. Some improvements could be the use of a colormap, rather than a fored gray scale. An example in teh help would also be a good addition.
I have started to try and make a combined plot which allows for both boxplot (using boxplotCsub) and distributionPlot. As both are symetrical, they can both be collapsed to one-sided and then combing, giving two very interesting looks at the same data sets.

Added option to align the bars at the left or the right (option "histOri"), as suggested by Yuri. Also, bugfix.

2 Oct 2011

1.9.0.0

Improved normalization options. Thanks to Jake for the suggestion.

21 Jun 2011

1.7.0.0

Fixed a bug in the code, and two mistakes in the example.

20 Jun 2011

1.6.0.0

Made colorbar more meaningful if there is only one colormap and the bins are normalized globally (i.e. globalNorm is set to 1). Thanks to Brian Katz for the suggestion.

20 Jun 2011

1.4.0.0

Changed input from optional arguments to parameterName/parameterValue pairs (note that the old syntax still works!).
Added several new features, such as support for grouped variables, overlay of data points, and user-defined colormaps.

20 Jan 2011

1.3.0.0

Updated title to Violin Plot, because that's how (part) of these plots are called elsewhere.